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The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.

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Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal sound samples. Recent discriminative models based on machine identity (ID) classification have shown excellent ASD performance by leveraging strong prior knowledge like machine ID. However, such strong priors are often unavailable in real-world applications, limiting these models. To address this, we propose utilizing the imbalanced and inconsistent attribute labels from acoustic sensors, such as machine running speed and microphone model, as weak priors to train an attribute classifier. We also introduce an imbalanced compensation strategy to handle extremely imbalanced categories and ensure model trainability. Furthermore, we propose a score fusion method to enhance anomaly detection robustness. The proposed algorithm was applied in our DCASE2023 Challenge Task 2 submission, ranking sixth internationally. By exploiting acoustic sensor data attributes as weak prior knowledge, our approach provides an effective framework for robust ASD when strong priors are absent.

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MAIN CONCLUSION: Specific sound patterns can affect plant development. Plants are responsive to environmental stimuli such as sound. However, little is known about their sensory apparatus, mechanisms, and signaling pathways triggered by these stimuli. Thus, it is important to understand the effect of sounds on plants and their technological potential. This review addresses the effects of sounds on plants, the sensory elements inherent to sound detection by the cell, as well as the triggering of signaling pathways that culminate in plant responses. The importance of sound standardization for the study of phytoacoustics is demonstrated. Studies on the sounds emitted or reflected by plants, acoustic stress in plants, and recognition of some sound patterns by plants are also explored.

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The emerging use of low-temperature plasma in medicine, especially in wound treatment, calls for a better way of documenting the treatment parameters. This paper describes the development of a mobile sensory device (referred to as MSD) that can be used during the treatment to ease the documentation of important parameters in a streamlined process. These parameters include the patient's general information, plasma source device used in the treatment, plasma treatment time, ambient humidity and temperature. MSD was developed as a standalone Raspberry Pi-based version and attachable module version for laptops and tablets. Both versions feature a user-friendly GUI, temperature-humidity sensor, microphone, treatment report generation and export. For the logging of plasma treatment time, a sound-based plasma detection system was developed, initially for three medically certified plasma source devices: kINPen® MED, plasma care®, and PlasmaDerm® Flex. Experimental validation of the developed detection system shows accurate and reliable detection is achievable at 5 cm measurement distance in quiet and noisy environments for all devices. All in all, the developed tool is a first step to a more automated, integrated, and streamlined approach of plasma treatment documentation that can help prevent user variability.

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With the emergence of many grave Chronic obstructive pulmonary diseases (COPDs) and the COVID-19 pandemic, there is a need for timely detection of abnormal respiratory sounds, such as deep and heavy breaths. Although numerous efficient pervasive healthcare systems have been proposed for tracking patients, few studies have focused on these breaths. This paper presents a method that supports physicians in monitoring in-hospital and at-home patients by monitoring their breath. The proposed method is based on three deep neural networks in audio analysis: RNNoise for noise suppression, SincNet - Convolutional Neural Network, and Residual Bidirectional Long Short-Term Memory for breath sound analysis at edge devices and centralized servers, respectively. We also developed a pervasive system with two configurations: (i) an edge architecture for in-hospital patients; and (ii) a central architecture for at-home ones. Furthermore, a dataset, named BreathSet, was collected from 27 COPD patients being treated at three hospitals in Vietnam to verify our proposed method. The experimental results demonstrated that our system efficiently detected and classified breath sounds with F1-scores of 90% and 91% for the tiny model version on low-cost edge devices, and 90% and 95% for the full model version on central servers, respectively. The proposed system was successfully implemented at hospitals to help physicians in monitoring respiratory patients in real time.

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A glass-diaphragm microphone was developed based on fiber-optic Fabry-Perot (FP) interferometry. The glass diaphragm was shaped into a wheel-like structure on a 150-µm-thick glass sheet by laser cutting, which consists of a glass disc connected to an outer glass ring by four identical glass beams. Such a structural diaphragm offers the microphone an open air chamber that reduces air damping and increases sensitivity and results in a cardioid direction pattern for the microphone response. The prepared microphone operates at 1550 nm wavelength, showing high stability in a range of temperature from 10 to 40 °C. The microphone has a resonance peak at 1152 Hz with a quality factor of 21, and its 3-dB cut-off frequency is 32 Hz. At normal incidence of 500 Hz sound, the pressure sensitivity of the microphone is 755 mV/Pa and the corresponding minimum detectable pressure is 251 µPa/Hz1/2. In addition to the above characteristics of the microphone in air, a preliminary investigation reveals that the microphone can also work stably under water for a long time due to the combination of the open-chamber and fiber-optic structures, and it has a large signal-to-noise ratio in response to waterborne sounds. The microphone prepared in this work is simple, inexpensive, and electromagnetically robust, showing great potential for low-frequency acoustic detection in air and under water.

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Flexible strain sensors based on 2D materials have been proven effective for wearable health monitoring devices, human motion detection, and fitness applications. These sensors are flexible, light, and user-friendly, but their sensitivity and detection range need to be enhanced. Among many 2D materials, MXene attracts much interest due to its remarkable properties, such as high electrical conductivity, excellent mechanical properties, flexibility, and good hydrophilicity. However, it is a challenge to fabricate strain sensors with extreme sensitivity and a wide sensing range. In this work, a multifunctional, cost-effective, and highly sensitive PDMS-encapsulated MXene@polyester fabric strain sensor was fabricated. Firstly, complete adsorption of MXene within the fabric formed conductive networks, and then PDMS was used to endow superhydrophobicity and corrosion resistance. The strain sensor demonstrated multifunctional applications and outstanding performance, such as long-term stability (over 500 cycles) and a wide sensing range (8%). The proposed sensor has promising potential for wearable electronic devices such as health monitoring systems and physiological sensing applications.

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The effect of adding milk on the structure and texture properties of six commercial extruded breakfast cereals was evaluated using instrumental (mechanical and acoustic) and sensory analyses, as well as the correlations between such measurements. Adding milk reduced the force and acoustic properties of the breakfast cereals and affected sensory acceptance, improving or damaging the texture attribute acceptance depending on the product. Regarding sensory and instrumental correlations, the guillotine Blade Set probe stood out for correlations between instrumental and sensory (both descriptive and acceptance) results, followed by the Kramer probe that provided correlations with sensory acceptance. All correlations were positive except for the intensity of adhesiveness, which means that the intensity of adhesiveness was the most critical attribute for the acceptance of breakfast cereals when milk is added. In conclusion, adding milk impacted the texture properties of breakfast cereals and the definition of the best probe to be used depends on the sensory characteristic to be evaluated and, also, on whether milk is added or not.

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Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains. It is also very difficult to detect lung or heart conditions with small amounts of data or unbalanced and high noise in data. Furthermore, the quality of data is a considerable pitfall for improving the performance of deep learning. In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds. The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets. The innovation of the proposed transfer learning relies on the transformation from audio data to image vectors and from three specific models to one fused model that would be more suitable for deep learning. We used two publicly available datasets for this study, i.e., lung sound data from ICHBI 2017 challenge and heart challenge data. We applied data augmentation techniques, such as noise distortion, pitch shift, and time stretching, dealing with some data issues in these datasets. Importantly, we extracted three unique features from the audio samples, i.e., Spectrogram, MFCC, and Chromagram. Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features. We confirmed the superiority of the proposed fusion model compared to the state-of-the-art works. The highest accuracy we achieved with FDC-FS is 99.1% with Spectrogram-based lung sound classification while 97% for Spectrogram and Chromagram based heart sound classification.

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The ideal development direction of the fiber-optic acoustic sensor (FOAS) is toward broadband, a high sensitivity and a large dynamic range. In order to further promote the acoustic detection potential of the Fabry-Pérot etalon (FPE)-based FOAS, it is of great significance to study the acoustic performance of the FOAS with the quality (Q) factor of FPE as the research objective. This is because the Q factor represents the storage capability and loss characteristic of the FPE. The three FOASs with different Q factors all achieve a broadband response from 20 Hz to 70 kHz with a flatness of ±2 dB, which is consistent with the theory that the frequency response of the FOAS is not affected by the Q factor. Moreover, the sensitivity of the FOAS is proportional to the Q factor. When the Q factor is 1.04×106, the sensitivity of the FOAS is as high as 526.8 mV/Pa. Meanwhile, the minimum detectable sound pressure of 347.33 µPa/Hz1/2  is achieved. Furthermore, with a Q factor of 0.27×106, the maximum detectable sound pressure and dynamic range are 152.32 dB and 107.2 dB, respectively, which is greatly improved compared with two other FOASs. Separately, the FOASs with different Q factors exhibit an excellent acoustic performance in weak sound detection and high sound pressure detection. Therefore, different acoustic detection requirements can be met by selecting the appropriate Q factor, which further broadens the application range and detection potential of FOASs.

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Duck eggs are a good source of essential nutrients for the human body. However, transportation, processing, and handling can easily cause cracks in the eggshells. These cracks can lead to microbial contamination, reducing the shelf life and compromising food safety. In this study, a method for the nondestructive testing of cracks in duck eggshells was developed. First, the acoustic emission signals of intact and cracked eggshells were measured, and the most significant frequency features were selected to establish a calibration curve for cracked eggshells. Logistic regression using the frequency features was then adopted to predict intact and cracked eggshells. Then, we establish a set of optimal regression models and used independent samples for verification. The overall accuracy rates of the calibration and prediction models using five frequencies of bandwidth (1500, 5000, 6000, 8500, and 10,000 Hz) were 89.7% and 87.6%, respectively. Sound measurement enables a simple and quantitative method for duck egg crack detection and classification. This nondestructive and cost-effective method can be used for duck egg quality screening and can be integrated into duck egg processing machinery.

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Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.

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This research introduces an idea of producing both nanoscale and microscale pores in piezoelectric material, and combining the properties of the molecular ß-phase dipoles in ferroelectric material and the space charge dipoles in order to increase the sensitivity of the sensor and modulate the response frequency bandwidth of the material. Based on this idea, a bi-nano-micro porous dual ferro-electret hybrid self-powered flexible heart sound detection sensor is proposed. Acid etching and electrospinning were the fabrication processes used to produce a piezoelectric film with nanoscale and microscale pores, and corona poling was used for air ionization to produce an electret effect. In this paper, the manufacturing process of the sensor is introduced, and the effect of the porous structure and corona poling on improving the performance of the sensor is discussed. The proposed flexible sensor has an equivalent piezoelectric coefficient d33 of 3312 pC/N, which is much larger than the piezoelectric coefficient of the common piezoelectric materials. Experiments were carried out to verify the function of the flexible sensor together with the SS17L heart sound sensor (BIOPAC, Goleta, CA, USA) as a reference. The test results demonstrated its practical application for wearable heart sound detection and the potential for heart disease detection. The proposed flexible sensor in this paper could realize batch production, and has the advantages of flexibility, low production cost and a short processing time compared with the existing heart sound detection sensors.

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Detecting sounds in quiet is arguably the simplest task performed by an auditory system, but the underlying mechanisms are still a matter of debate. Threshold stimulus levels depend not only on the physical properties of the sounds to be detected but also on the experimental procedure used to measure them. Here, thresholds of human subjects were measured for sounds consisting of different numbers of bursts using both an alternative-forced-choice and a yes-no procedure in the same experimental sessions. Thresholds measured with the yes-no procedure were typically higher than thresholds measured with the alternative-forced choice procedure. The difference between the two thresholds decreased as stimulus duration increased. It also varied between subjects and varied with the probability of false alarms in the yes-no procedure. It is shown that a previously proposed model of detection (Heil et al., Hear Res 2017) can account for these findings better than other models. It can also account for the shapes of the psychometric functions. The model is consistent with basic concepts of signal detection theory but is based on a decision variable that follows Poisson statistics. It also differs from other models of detection with respect to the transformation of the stimulus into the decision variable. The findings in this study further support the model.

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BACKGROUND: Automated sound recorders are a popular sampling tool in ecology. However, the microphones themselves received little attention so far, and specifications that determine the recordings' sound quality are seldom mentioned. Here, we demonstrate the importance of microphone signal-to-noise ratio for sampling sonant animals. METHODS: We tested 12 different microphone models in the field and measured their signal-to-noise ratios and detection ranges. We also measured the vocalisation activity of birds and bats that they recorded, the bird species richness, the bat call types richness, as well as the performance of automated detection of bird and bat calls. We tested the relationship of each one of these measures with signal-to-noise ratio in statistical models. RESULTS: Microphone signal-to-noise ratio positively affects the sound detection space areas, which increased by a factor of 1.7 for audible sound, and 10 for ultrasound, from the lowest to the highest signal-to-noise ratio microphone. Consequently, the sampled vocalisation activity increased by a factor of 1.6 for birds, and 9.7 for bats. Correspondingly, the species pool of birds and bats could not be completely detected by the microphones with lower signal-to-noise ratio. The performance of automated detection of bird and bat calls, as measured by its precision and recall, increased significantly with microphone signal-to-noise ratio. DISCUSSION: Microphone signal-to-noise ratio is a crucial characteristic of a sound recording system, positively affecting the acoustic sampling performance of birds and bats. It should be maximised by choosing appropriate microphones, and be quantified independently, especially in the ultrasound range.

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Phase fading is fatal to the performance of distributed acoustic sensors (DASs) influencing its capability of distributed measurement as well as its noise level. Here, we report the experimental observation of a strong negative correlation between the relative power spectrum density (PSD) at the heterodyne frequency and the noise floor of the detected phase for the heterodyne demodulated distributed acoustic sensor (HD-DAS) system. We further propose a weighted-channel stack algorithm (WCSA) to alleviate the phase fading noise via an enhancement of the relative PSD at the heterodyne frequency. Experimental results show that the phase noise of the demodulated signal can be suppressed by 13.7 dB under optimal condition. As a potential application, we exploited the improved HD-DAS system to retrieve a piece of music lasted for 205 s, demonstrating the reliability of detecting wideband sound signal without distortion.

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Underwater sound is directional and can convey important information about the surrounding environment or the animal emitting the sound. Therefore, sound is a major sensory channel for fishes and plays a key role in many life-history strategies. The effect of anthropogenic noise on aquatic life, which may be causing homogenisation or fragmentation of biologically important signals underwater is of growing concern. In this review we discuss the role sound plays in the ecology of fishes, basic anatomical and physiological adaptations for sound reception and production, the effects of anthropogenic noise and how fishes may be coping to changes in their environment, to put the ecology of fish hearing into the context of the modern underwater soundscape.

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Auditory selective attention can be directed toward spatial and non-spatial stimulus features. Here, we studied electrophysiological correlates of spatial attention under spatially-specific and purely feature-based demands. Using an auditory search paradigm, in which participants performed a target localization (left versus right) and a target detection task (present versus absent), we investigated whether attentional selection of a relevant sound from a two- or four-sound array necessarily involves the processing of spatial sound information. While the early N2 anterior contralateral component occurred irrespective of task, the subsequent lateralization of alpha power oscillations (8-12 Hz) over parieto-occipital scalp was modulated by the task-relevance of spatial information. Thus, the two correlates appear to reflect differential aspects of attentional orienting: We propose that the N2ac reflects an initial, modality-specific focusing of attention onto a lateralized target, while the subsequent alpha lateralization appears associated with the spatiotopic access to presumably supramodal representations of the sound array.

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We use recent research to provide an explanation of how animals might detect earthquakes before they occur. While the intrinsic value of such warnings is immense, we show that the complexity of the process may result in inconsistent responses of animals to the possible precursor signal. Using the results of our research, we describe a logical but complex sequence of geophysical events triggered by precursor earthquake crustal movements that ultimately result in a sound signal detectable by animals. The sound heard by animals occurs only when metal or other surfaces (glass) respond to vibrations produced by electric currents induced by distortions of the earth's electric fields caused by the crustal movements. A combination of existing measurement systems combined with more careful monitoring of animal response could nevertheless be of value, particularly in remote locations.

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OBJECTIVES: To evaluate the reliability of monolithic and multilayer ceramic structures used in the CAD-on technique (Ivoclar), and the mode of failure produced in ceramic structures bonded to a dentin analog material (NEMA-G10). METHODS: Ceramic specimens were fabricated as follows (n=30): CAD-on- trilayer structure (IPS e.max ZirCAD/IPS e.max Crystall./Connect/IPS e.max CAD); YLD- bilayer structure (IPS e.max ZirCAD/IPS e.max Ceram); LDC- monolithic structure (IPS e.max CAD); and YZW- monolithic structure (Zenostar Zr Translucent). All ceramic specimens were bonded to G10 and subjected to compressive load in 37°C distilled water until the sound of the first crack, monitored acoustically. Failure load (Lf) values were recorded (N) and statistically analyzed using Weibull distribution, Kruskal-Wallis test, and Student-Newman-Keuls test (α=0.05). RESULTS: Lf values of CAD-on and YZW structures were statistically similar (p=0.917), but higher than YLD and LDC (p<0.01). Weibull modulus (m) values were statistically similar for all experimental groups. Monolithic structures (LDC and YZW) failed from radial cracks. Failures in the CAD-on and YLD groups showed, predominantly, both radial and cone cracks. SIGNIFICANCE: Monolithic zirconia (YZW) and CAD-on structures showed similar failure resistance and reliability, but a different fracture behavior.

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